Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Alerts

Age is highly overall correlated with PregnanciesHigh correlation
Insulin is highly overall correlated with SkinThicknessHigh correlation
Pregnancies is highly overall correlated with AgeHigh correlation
SkinThickness is highly overall correlated with InsulinHigh correlation
Pregnancies has 111 (14.5%) zeros Zeros
BloodPressure has 35 (4.6%) zeros Zeros
SkinThickness has 227 (29.6%) zeros Zeros
Insulin has 374 (48.7%) zeros Zeros
BMI has 11 (1.4%) zeros Zeros

Reproduction

Analysis started2025-03-30 04:51:26.533073
Analysis finished2025-03-30 04:51:28.585018
Duration2.05 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Pregnancies
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8450521
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-03-29T21:51:28.601372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3695781
Coefficient of variation (CV)0.87634133
Kurtosis0.15921978
Mean3.8450521
Median Absolute Deviation (MAD)2
Skewness0.90167398
Sum2953
Variance11.354056
MonotonicityNot monotonic
2025-03-29T21:51:28.623693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 135
17.6%
0 111
14.5%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
Other values (7) 58
7.6%
ValueCountFrequency (%)
0 111
14.5%
1 135
17.6%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
ValueCountFrequency (%)
17 1
 
0.1%
15 1
 
0.1%
14 2
 
0.3%
13 10
 
1.3%
12 9
 
1.2%
11 11
 
1.4%
10 24
3.1%
9 28
3.6%
8 38
4.9%
7 45
5.9%

Glucose
Real number (ℝ)

Distinct136
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.89453
Minimum0
Maximum199
Zeros5
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-03-29T21:51:28.650061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q199
median117
Q3140.25
95-th percentile181
Maximum199
Range199
Interquartile range (IQR)41.25

Descriptive statistics

Standard deviation31.972618
Coefficient of variation (CV)0.26446703
Kurtosis0.64077982
Mean120.89453
Median Absolute Deviation (MAD)20
Skewness0.1737535
Sum92847
Variance1022.2483
MonotonicityNot monotonic
2025-03-29T21:51:28.680190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 17
 
2.2%
100 17
 
2.2%
111 14
 
1.8%
129 14
 
1.8%
125 14
 
1.8%
106 14
 
1.8%
112 13
 
1.7%
108 13
 
1.7%
95 13
 
1.7%
105 13
 
1.7%
Other values (126) 626
81.5%
ValueCountFrequency (%)
0 5
0.7%
44 1
 
0.1%
56 1
 
0.1%
57 2
 
0.3%
61 1
 
0.1%
62 1
 
0.1%
65 1
 
0.1%
67 1
 
0.1%
68 3
0.4%
71 4
0.5%
ValueCountFrequency (%)
199 1
 
0.1%
198 1
 
0.1%
197 4
0.5%
196 3
0.4%
195 2
0.3%
194 3
0.4%
193 2
0.3%
191 1
 
0.1%
190 1
 
0.1%
189 4
0.5%

BloodPressure
Real number (ℝ)

Zeros 

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.105469
Minimum0
Maximum122
Zeros35
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-03-29T21:51:28.709074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.7
Q162
median72
Q380
95-th percentile90
Maximum122
Range122
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.355807
Coefficient of variation (CV)0.28009082
Kurtosis5.1801566
Mean69.105469
Median Absolute Deviation (MAD)8
Skewness-1.843608
Sum53073
Variance374.64727
MonotonicityNot monotonic
2025-03-29T21:51:28.738254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
70 57
 
7.4%
74 52
 
6.8%
78 45
 
5.9%
68 45
 
5.9%
72 44
 
5.7%
64 43
 
5.6%
80 40
 
5.2%
76 39
 
5.1%
60 37
 
4.8%
0 35
 
4.6%
Other values (37) 331
43.1%
ValueCountFrequency (%)
0 35
4.6%
24 1
 
0.1%
30 2
 
0.3%
38 1
 
0.1%
40 1
 
0.1%
44 4
 
0.5%
46 2
 
0.3%
48 5
 
0.7%
50 13
 
1.7%
52 11
 
1.4%
ValueCountFrequency (%)
122 1
 
0.1%
114 1
 
0.1%
110 3
0.4%
108 2
0.3%
106 3
0.4%
104 2
0.3%
102 1
 
0.1%
100 3
0.4%
98 3
0.4%
96 4
0.5%

SkinThickness
Real number (ℝ)

High correlation  Zeros 

Distinct51
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.536458
Minimum0
Maximum99
Zeros227
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-03-29T21:51:28.768174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q332
95-th percentile44
Maximum99
Range99
Interquartile range (IQR)32

Descriptive statistics

Standard deviation15.952218
Coefficient of variation (CV)0.77677549
Kurtosis-0.52007187
Mean20.536458
Median Absolute Deviation (MAD)12
Skewness0.1093725
Sum15772
Variance254.47325
MonotonicityNot monotonic
2025-03-29T21:51:28.799403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 227
29.6%
32 31
 
4.0%
30 27
 
3.5%
27 23
 
3.0%
23 22
 
2.9%
33 20
 
2.6%
28 20
 
2.6%
18 20
 
2.6%
31 19
 
2.5%
19 18
 
2.3%
Other values (41) 341
44.4%
ValueCountFrequency (%)
0 227
29.6%
7 2
 
0.3%
8 2
 
0.3%
10 5
 
0.7%
11 6
 
0.8%
12 7
 
0.9%
13 11
 
1.4%
14 6
 
0.8%
15 14
 
1.8%
16 6
 
0.8%
ValueCountFrequency (%)
99 1
 
0.1%
63 1
 
0.1%
60 1
 
0.1%
56 1
 
0.1%
54 2
0.3%
52 2
0.3%
51 1
 
0.1%
50 3
0.4%
49 3
0.4%
48 4
0.5%

Insulin
Real number (ℝ)

High correlation  Zeros 

Distinct186
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.799479
Minimum0
Maximum846
Zeros374
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-03-29T21:51:28.830136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30.5
Q3127.25
95-th percentile293
Maximum846
Range846
Interquartile range (IQR)127.25

Descriptive statistics

Standard deviation115.244
Coefficient of variation (CV)1.4441699
Kurtosis7.2142596
Mean79.799479
Median Absolute Deviation (MAD)30.5
Skewness2.2722509
Sum61286
Variance13281.18
MonotonicityNot monotonic
2025-03-29T21:51:28.860053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 374
48.7%
105 11
 
1.4%
130 9
 
1.2%
140 9
 
1.2%
120 8
 
1.0%
94 7
 
0.9%
180 7
 
0.9%
100 7
 
0.9%
135 6
 
0.8%
115 6
 
0.8%
Other values (176) 324
42.2%
ValueCountFrequency (%)
0 374
48.7%
14 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
18 2
 
0.3%
22 1
 
0.1%
23 2
 
0.3%
25 1
 
0.1%
29 1
 
0.1%
32 1
 
0.1%
ValueCountFrequency (%)
846 1
0.1%
744 1
0.1%
680 1
0.1%
600 1
0.1%
579 1
0.1%
545 1
0.1%
543 1
0.1%
540 1
0.1%
510 1
0.1%
495 2
0.3%

BMI
Real number (ℝ)

Zeros 

Distinct248
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.992578
Minimum0
Maximum67.1
Zeros11
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-03-29T21:51:28.887653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.8
Q127.3
median32
Q336.6
95-th percentile44.395
Maximum67.1
Range67.1
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation7.8841603
Coefficient of variation (CV)0.24643717
Kurtosis3.2904429
Mean31.992578
Median Absolute Deviation (MAD)4.6
Skewness-0.42898159
Sum24570.3
Variance62.159984
MonotonicityNot monotonic
2025-03-29T21:51:28.959427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 13
 
1.7%
31.6 12
 
1.6%
31.2 12
 
1.6%
0 11
 
1.4%
32.4 10
 
1.3%
33.3 10
 
1.3%
30.1 9
 
1.2%
32.8 9
 
1.2%
32.9 9
 
1.2%
30.8 9
 
1.2%
Other values (238) 664
86.5%
ValueCountFrequency (%)
0 11
1.4%
18.2 3
 
0.4%
18.4 1
 
0.1%
19.1 1
 
0.1%
19.3 1
 
0.1%
19.4 1
 
0.1%
19.5 2
 
0.3%
19.6 3
 
0.4%
19.9 1
 
0.1%
20 1
 
0.1%
ValueCountFrequency (%)
67.1 1
0.1%
59.4 1
0.1%
57.3 1
0.1%
55 1
0.1%
53.2 1
0.1%
52.9 1
0.1%
52.3 2
0.3%
50 1
0.1%
49.7 1
0.1%
49.6 1
0.1%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-03-29T21:51:28.988327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.3313286
Coefficient of variation (CV)0.70215138
Kurtosis5.5949535
Mean0.4718763
Median Absolute Deviation (MAD)0.1675
Skewness1.9199111
Sum362.401
Variance0.10977864
MonotonicityNot monotonic
2025-03-29T21:51:29.017162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.258 6
 
0.8%
0.254 6
 
0.8%
0.268 5
 
0.7%
0.207 5
 
0.7%
0.261 5
 
0.7%
0.259 5
 
0.7%
0.238 5
 
0.7%
0.19 4
 
0.5%
0.263 4
 
0.5%
0.299 4
 
0.5%
Other values (507) 719
93.6%
ValueCountFrequency (%)
0.078 1
0.1%
0.084 1
0.1%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.1%
0.092 1
0.1%
0.096 1
0.1%
0.1 1
0.1%
0.101 1
0.1%
0.102 1
0.1%
ValueCountFrequency (%)
2.42 1
0.1%
2.329 1
0.1%
2.288 1
0.1%
2.137 1
0.1%
1.893 1
0.1%
1.781 1
0.1%
1.731 1
0.1%
1.699 1
0.1%
1.698 1
0.1%
1.6 1
0.1%

Age
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.240885
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-03-29T21:51:29.044122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.760232
Coefficient of variation (CV)0.35378816
Kurtosis0.64315889
Mean33.240885
Median Absolute Deviation (MAD)7
Skewness1.1295967
Sum25529
Variance138.30305
MonotonicityNot monotonic
2025-03-29T21:51:29.073671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 72
 
9.4%
21 63
 
8.2%
25 48
 
6.2%
24 46
 
6.0%
23 38
 
4.9%
28 35
 
4.6%
26 33
 
4.3%
27 32
 
4.2%
29 29
 
3.8%
31 24
 
3.1%
Other values (42) 348
45.3%
ValueCountFrequency (%)
21 63
8.2%
22 72
9.4%
23 38
4.9%
24 46
6.0%
25 48
6.2%
26 33
4.3%
27 32
4.2%
28 35
4.6%
29 29
3.8%
30 21
 
2.7%
ValueCountFrequency (%)
81 1
 
0.1%
72 1
 
0.1%
70 1
 
0.1%
69 2
0.3%
68 1
 
0.1%
67 3
0.4%
66 4
0.5%
65 3
0.4%
64 1
 
0.1%
63 4
0.5%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
0
500 
1
268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Length

2025-03-29T21:51:29.098448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T21:51:29.116721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring characters

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Interactions

2025-03-29T21:51:28.168159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:26.840191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.049126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.241970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.421243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.634677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.803227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.987438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.192352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:26.873749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.074583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.266364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.443545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.656029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.827205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.010484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.216204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:26.902127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.100390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.289422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.466714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.678243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.851508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.034439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.239612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:26.927141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.124722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.312324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.487763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.698671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.874066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.056787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.262307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:26.952038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.147676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.333776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.508696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.719241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.896566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.078496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.325898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:26.974949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.170025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.354303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.529360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.739028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.917848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.100194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.348742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.000424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.194392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.377145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.591521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.761118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.941451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.122869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.372500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.024561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.218513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.399085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.613287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.782294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:27.964762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-29T21:51:28.145078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-29T21:51:29.131181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeBMIBloodPressureDiabetesPedigreeFunctionGlucoseInsulinOutcomePregnanciesSkinThickness
Age1.0000.1310.3510.0430.285-0.1140.3140.607-0.067
BMI0.1311.0000.2930.1410.2310.1930.3170.0000.444
BloodPressure0.3510.2931.0000.0300.235-0.0070.1520.1850.126
DiabetesPedigreeFunction0.0430.1410.0301.0000.0910.2210.173-0.0430.180
Glucose0.2850.2310.2350.0911.0000.2130.4870.1310.060
Insulin-0.1140.193-0.0070.2210.2131.0000.159-0.1270.541
Outcome0.3140.3170.1520.1730.4870.1591.0000.2350.208
Pregnancies0.6070.0000.185-0.0430.131-0.1270.2351.000-0.085
SkinThickness-0.0670.4440.1260.1800.0600.5410.208-0.0851.000

Missing values

2025-03-29T21:51:28.407056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-29T21:51:28.431170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
061487235033.60.627501
11856629026.60.351310
28183640023.30.672321
318966239428.10.167210
40137403516843.12.288331
55116740025.60.201300
637850328831.00.248261
71011500035.30.134290
82197704554330.50.158531
9812596000.00.232541
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7581106760037.50.197260
7596190920035.50.278661
76028858261628.40.766220
76191707431044.00.403431
762989620022.50.142330
76310101764818032.90.171630
76421227027036.80.340270
7655121722311226.20.245300
7661126600030.10.349471
7671937031030.40.315230